As AI capabilities and deployment accelerate toward a post-AGI era, concerns are growing about electricity demand and carbon emissions from AI computing, yet it is rarely represented explicitly in long term energy-economy-climate scenario models. In such a setting, digital infrastructure scaling may be constrained by power system dynamics. We introduce an AI computing sector into the Global Change Analysis Model (GCAM) and run U.S. scenarios that couple AI service growth with time varying compute energy intensity and economic drivers. We find that service growth does not translate linearly into electricity demand: outcomes depend on efficiency trajectories and demand responsiveness. With sustained efficiency improvements, AI electricity demand remains moderated; with slower or saturating gains, income-driven demand dominates by mid-century. Sensitivity analyses show weak responsiveness to price signals but strong dependence on income growth, implying limited leverage from price-based mechanisms alone. Rather than offering a single forecast, we map conditions under which efficiency-dominant versus demand-dominant regimes emerge, providing a compact template for long run AI electricity-demand scenarios and their implications for power sector emissions.

翻译：随着人工智能能力与部署加速迈向后通用人工智能时代，人工智能计算带来的电力需求与碳排放问题日益引发关注，但这一因素在长期能源-经济-气候情景模型中鲜少得到明确表征。在此背景下，数字基础设施的扩展可能受到电力系统动态特性的制约。本研究将人工智能计算部门引入全球变化分析模型（GCAM），通过耦合人工智能服务增长、随时间变化的计算能源强度及经济驱动因素，对美国情景进行模拟分析。研究发现：服务增长并不线性转化为电力需求，实际结果取决于效率演进路径与需求响应特性。在持续能效提升的情景下，人工智能电力需求将保持温和增长；若能效提升放缓或趋于饱和，则至本世纪中叶收入驱动型需求将占据主导。敏感性分析显示，人工智能电力需求对价格信号响应较弱，但对收入增长具有强依赖性，这意味着单纯依靠价格调控机制的作用空间有限。本研究并未提供单一预测，而是系统刻画了效率主导型与需求主导型两种机制的形成条件，为构建长期人工智能电力需求情景及其对电力部门排放影响的紧凑分析框架提供了模板。